ARCHIVES

1. Gupta, A., Kumar, R., Singh Arora, H., & Raman, B. (2020). MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis.
IEEE Access, 8(Ml), 14659–14674. https://doi.org/10.1109/ACCESS.2019.2962755
2. Guo, C., Zhang, J., Liu, Y., Xie, Y., Han, Z., & Yu, J. (2020). Recursion Enhanced Random Forest with an Improved Linear Model
(RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform. IEEE Access, 8, 59247–59256.
https://doi.org/10.1109/ACCESS.2020.2981159
3. Kavitha, R., & Kannan, E. (2016). An efficient framework for heart disease classification using feature extraction and feature selection
technique in data mining. 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 -
Proceedings. https://doi.org/10.1109/ICETETS.2016.7603000
4. Javeed, A., Zhou, S., Yongjian, L., Qasim, I., Noor, A., & Nour, R. (2019). An Intelligent Learning System Based on Random Search
Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection. IEEE Access, 7, 180235–180243.
https://doi.org/10.1109/ACCESS.2019.2952107
5. Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE
Access, 7, 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707
6. Krishnani, D., Kumari, A., Dewangan, A., Singh, A., & Naik, N. S. (2019). Prediction of Coronary Heart Disease using Supervised
Machine Learning Algorithms. IEEE Region 10 Annual International Conference, Proceedings/ TENCON, 2019-October, 367–372.
https://doi.org/10.1109/TENCON.2019.8929434
7. Gudadhe, M., Wankhade, K., & Dongre, S. (2010). Decision support system for heart disease based on support vector machine and
artificial neural network. 2010 International Conference on Computer and Communication Technology, ICCCT-2010, 741–745.
https://doi.org/10.1109/ICCCT.2010.5640377
8. Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., & Wang, Q. (2017). A Hybrid Classification System for Heart Disease Diagnosis
Based on the RFRS Method. Computational and Mathematical Methods in Medicine, 2017. https://doi.org/10.1155/2017/8272091
9. Haq, A. U., Li, J., Memon, M. H., Hunain Memon, M., Khan, J., & Marium, S. M. (2019). Heart Disease Prediction System Using Model
of Machine Learning and Sequential Backward Selection Algorithm for Features Selection. 2019 IEEE 5th International Conference for
Convergence in Technology, I2CT 2019, 1–4. https://doi.org/10.1109/I2CT45611.2019.9033683
10. Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: Criteria of Max-Dependency, Max-Relevance,
and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.
https://doi.org/10.1109/TPAMI.2005.159
11. Satu, M. S., Tasnim, F., Akter, T., & Halder, S. (2018). Exploring Significant Heart Disease Factors based on Semi Supervised Learning
Algorithms. International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018, 1–4. https://doi.org/10.1109/IC4ME2.2018.8465642
12. Sonawane, J. S., & Patil, D. R. (2015). Prediction of heart disease using multilayer perceptron neural network. 2014 International
Conference on Information Communication and Embedded Systems, ICICES 2014, 978. https://doi.org/10.1109/ICICES.2014.7033860
13. Juneja, K., & Rana, C. (2018). Feature expanded and weight selective model to classify the heart disease patients. 2018 2nd IEEE
International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2018, 962–966.
https://doi.org/10.1109/ICPEICES.2018.8897471
14. Chatterjee, S., Jaggi, Y., & Sowmiya, B. (2019). Survey on prediction of heart disease using data mining. Proceedings of the
International Conference on Intelligent Sustainable Systems, ICISS 2019, 341–344. https://doi.org/10.1109/ISS1.2019.8908062
15. Jabbar, M. A., & Samreen, S. (2017). Heart disease prediction system based on hidden naïve bayes classifier. 2016 International
Conference on Circuits, Controls, Communications and Computing, I4C 2016. https://doi.org/10.1109/CIMCA.2016.8053261
16. Bharti, S., & Singh, S. N. (2015). Analytical study of heart disease prediction comparing with different algorithms. International
Conference on Computing, Communication and Automation, ICCCA 2015, 78–82. https://doi.org/10.1109/CCAA.2015.7148347
17. Normawati, D., & Winarti, S. (2018). Feature selection with combination classifier use rules-based data mining for diagnosis of
coronary heart disease. Proceeding of 2018 12th International Conference on Telecommunication Systems, Services, and Applications,
TSSA 2018, 1–6. https://doi.org/10.1109/TSSA.2018.8708849
18. Khemphila, A., & Boonjing, V. (2011). Heart disease classification using neural network and feature selection. Proceedings - ICSEng
2011: International Conference on Systems Engineering, 2007, 406–409. https://doi.org/10.1109/ICSEng.2011.80
19. Rane, A. L. (2018). A survey on Intelligent Data Mining Techniques used in Heart Disease Prediction. Proceedings 2018 3rd
International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018, 208–213.
https://doi.org/10.1109/CSITSS.2018.8768735
20. Ali, S. A., Raza, B., Malik, A. K., Shahid, A. R., Faheem, M., Alquhayz, H., & Kumar, Y. J. (2020). An Optimally Configured and
Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo-Tompa and Stacked Genetic
Algorithm. IEEE Access, 8, 65947–65958. https://doi.org/10.1109/ACCESS.2020.2985646
21. Paul, A. K., Shill, P. C., Rabin, M. R. I., & Akhand, M. A. H. (2016). Genetic algorithm based fuzzy decision support system for the
diagnosis of heart disease. 2016 5th International Conference on Informatics, Electronics and Vision, ICIEV 2016, 145–150.
https://doi.org/10.1109/ICIEV.2016.7759984
22. Ali, L., Niamat, A., Khan, J. A., Golilarz, N. A., Xingzhong, X., Noor, A., Nour, R., & Bukhari, S. A. C. (2019). An Optimized Stacked
Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure. IEEE Access, 7, 54007–54014.
https://doi.org/10.1109/ACCESS.2019.2909969
23. Zhang, Y., Liu, F., Zhao, Z., Li, D., Zhou, X., & Wang, J. (2012). Studies on application of support vector machine in diagnose of
coronary heart disease. 2012 6th International Conference on Electromagnetic Field Problems and Applications, ICEF’2012.
https://doi.org/10.1109/ICEF.2012.6310380
24. Mo, Y., & Xu, S. (2010). Application of SVM based on hybrid kernel function in heart disease diagnoses. Proceedings - 2010
International Conference on Intelligent Computing and Cognitive Informatics, ICICCI 2010, 462–465.
https://doi.org/10.1109/ICICCI.2010.96
25. Sowmiya, C., & Sumitra, P. (2018). Analytical study of heart disease diagnosis using classification techniques. Proceedings of the 2017
IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2017, 2018-Febru, 1–
5. https://doi.org/10.1109/ITCOSP.2017.8303115
26. Meesri, S., Phimoltares, S., & Mahaweerawat, A. (2018). Diagnosis of Heart Disease Using a Mixed Classifier. ICSEC 2017 - 21st
International Computer Science and Engineering Conference 2017, Proceeding, 6, 118–123.
https://doi.org/10.1109/ICSEC.2017.8443940
27. Purushottam, Saxena, K., & Sharma, R. (2016). Efficient Heart Disease Prediction System. Procedia Computer Science, 85, 962–969.
https://doi.org/10.1016/j.procs.2016.05.288
28. Geweid, G. G. N., & Abdallah, M. A. (2019). A new automatic identification method of heart failure using improved support vector
machine based on duality optimization technique. IEEE Access, 7, 149595–149611. https://doi.org/10.1109/ACCESS.2019.2945527